Schlafly Statistics is one of the favorite "debating tactics" of Andrew Schlafly, the founder and braindead braintrust behind Conservapedia. It consists of making up an utterly spurious and unfounded correlativestatistics in a bizarre attempt to undermine the argument of someone who is completely schooling him, usually but not always an expert in the field under discussion. This is basically a clumsily disguised ad hominem attack, and even if the "statistic" were true, it would have no bearing on the matter at hand.

Schlafly statistics are infallible as Andrew Schlafly has taken twice as many statistics courses as anyone else[1] -- he is extremely modest, too.

The National Academy of Science have described the attempt to publish Schlafly Statistics as such:

“”The issues raised by Mr. Schlafly are neither obscure nor subtle, but are part of everyday statistical analysis at a level too elementary to need rehearsal in the pages of PNAS[2].

Andy here fails at basic math. To quote, "Polls show that about twice as many Americans identify themselves as "conservative" compared with "liberal", and that ratio has been increasing for two decades. But on Wikipedia, about three times as many editors identify themselves as "liberal" compared with "conservative". That suggests Wikipedia is six times more liberal than the American public."[3] We shall now proceed to utilize the power of mathematics to demonstrate that this is wrong.

Andy tries to justify this by using something he calls "liberal quotient". This is the ratio of liberals in the group to conservatives. In America, this would be 1:2, and on Wikipedia, 3:1. Apparently, Andy has somehow neglected to realize that a ratio expressed in this form is not a fraction, and that the fractional description of the portion of a group composed of n, out of a group composed of n and q, is not actually n/q, it's n/(n+q). Therefore, instead of dividing 3/4ths (3:1 = 75%) by 1/3rd (1:2 = 33.333...%) which gives 2.25, he divides 3/1 by 1/2 and gets 6. In other words, he's said that 300% of Wikipedia editors are liberal. As such, this statistic is entirely fallacious.

Show me 100 people who are "ardent subscribers" to The Theory of Evolution and I'll show you 50 who welcome the creation of chimerae. Show me 100 people who have strong faith and I'll show you 99 who oppose this.--Aschlafly 12:38, 28 June 2007 (EDT)[4]

Show me a chimney sweep and I will show you someone with 100% (!) dirty nails.[5]

From a discussion of Benjamin Franklin's religious views on his talk page:

I’ll bet the correlation between those who insist Franklin was a deist and liberals is nearly 100%. - Aschlafly 12:22, 24 May 2007 (EDT)

James, please disclose your political point-of-view along with that of the “scholar”. Let me guess: do both of you oppose prayer in the classrooms of public schools? Enough said. - Aschlafly 13:11, 24 May 2007 (EDT)

We are discussing the past, not the political present. So, why bring up school prayer? What does that have to so with Franklin’s deism? – Jamest 13:17, 24 May 2007 (EDT)

It explains your views. If you’re opposed to prayer in the classroom, then there is over a 90% chance that (1) you’ll insist that Franklin was a deist and (2) you believe in evolution. That extraordinary correlation suggests that the facts are irrelevant. If you’re not open-minded about prayer in the classroom, then it’s futile to expect you to be open-minded about these other issues. – Aschlafly 18:41, 24 May 2007 (EDT)

At least he is correct on that last point - with regards to his own closed mind, that is.

I do know, with 95% certainty, what your positions are on classroom prayer and evolution. The 95% confidence level is all that science requires. I know your positions with greater certainty than I know what the weather will be like tomorrow. But why do you refuse to disclose your positions? Are you embarrassed about them? Do you feel they are indefensible? Conservatives don't hide their views; liberals do. Why? - Aschlafly 10:26, 25 May 2007 (EDT) [6]

Now he's up to 95% certainty. He just keeps getting better! Is he aware that scientists consider a 90% or 95% correlation very significant when it is a correlation of data collected from actual observations or experiments rather than just plucked out of the air by one person based on intuition alone? Aschlafly didn't go into the details of all the people he has observed these data on; which ones advocate classroom prayer, which ones accept evolution, and which ones believe Franklin was a deist.

In a main page talk discussion of Ron Paul and evolution, Mr. Schlafly loads for bear and uncorks this unsubstantiated beauty:

The correlation between believing in evolution and opposing classroom prayer is probably over 90%; believing in evolution and supporting abortion is probably over 80%, and believing in evolution and supporting gun control is probably over 70%. People who believe in evolution tend to be materialists, while conservatives tend to be Platonic. I've never met or heard of anyone who believed in evolution and was a 100%, across-the-board conservative like Ronald Reagan, Ron Paul, Jesse Helms, Tom Tancredo, Sam Brownback, etc." - Aschlafly 15:47, 4 January 2008 (EST)"[7]

We can infer the religion of a person by the majority religion of their countrymen 20 years after their death.[8] The relevant factors to consider when inferring someone's religion do not include: the assertions of their (liberal) family members, or the fact that they were Harvard-educated, but only their nationality. The possibility that a person would hold a minority religious viewpoint is inconceivable.

Note, by the way, that the odds against a Muslim converting to Christianity (as Obama essentially pretended) are greater than 100 to 1."[9]

Schlafly uses this method in order to create the impression that when someone makes a claim that is only true 1% of the time, it means there is a 99% chance they are lying. To turn this around on him, he claims to have a degree in electrical engineering as well as in law. Certainly far less than 1% of the population of the United States has both these degrees, therefore the probability that he is lying about this is much greater than 99%. The probability of him being gay or Muslim is much, much higher. (Additionally, he claims to be the son of Phyllis Schlafly, the odds of which are about 75,000,000 to 1, or in the neighborhood of .000001% likely.)

Just to explicitly say why this is wrong, if you haven't cottoned on yet, the point is that if someone says they are description X, it is much more likely that they are X than it would be if you just chose someone at random. For instance, very few people have a sister named Carmelita, but if someone says they have a sister named Carmelita, it's still likely that they're telling the truth, because the number of people who lie about having a sister called Carmelita is incredibly small.

On the list of Counterexamples to Evolution Schlafly showed how, using a list of statements that may or may not be true, over all the list must be true.

If just one is correct, then logically the theory of evolution must be false. If there is merely a 5% chance that each example is correct, then the odds of at least one of the following counterexamples being correct is nearly 100%. At 14 examples and a probability that each is correct being 5%, then the odds of at least one being correct is 1-(.95)^14.[10]

The first major problem is the assumption that the 14 items are independent, which they of course are not, as some or all are true if evolution is incorrect and all are false if evolution is correct. The second problem is that 1-(0.95)^14 is approximately 51%, which most people wouldn't describe as "nearly 100%". His third problem is he just made the 5% figure up, and it could be less. Finally, the problem we have seen many times in Schlafly's arguments, is that the truth is not a statistical function that can be determined by getting close enough to 100%. So long as the probability is not 0% there still exists a chance that it might be true[11].

Sampling is a process by which a representative subset of a population is studied in order to infer characteristics of the population as a whole. This is typically employed when the population would be too large to study, or when it's determined that the subset would provide acceptable levels of accuracy. The sample size must be representative if accurate inferences are to be made. For example, if wanting to understand the prevalence of beards in the population of London, examining just 10 chins is unlikely to yield representative results. The makeup of the sample is just as important. Would the results of a survey be useful if female chins were included, or if the men questioned all happened to be emerging from a local mosque? Schlafly does not trouble himself with such concerns.

In March 2011 Schlafly trumpeted the news that Wikipedia was only retaining 12% of its editors.[12] The fun began when Schlafly was asked for details on Conservapedia's editor retention rate.

Don't have a numeric answer, but based on experience our retention rate of quality editors is quite high. For example, there were three quality editors who registered at Conservapedia at the very beginning of the massive publicity of 2007. Two of those three (none of whom I have ever met personally) are still frequent editors today.[13]

Although clearly an informal and quick response to the question, the mere fact that he chose to give this answer demonstrates dishonesty or a complete misunderstanding of statistical analysis. He has arbitrarily chosen three "quality editors" who are what he describes as being "frequent editors". By this logic any wiki could claim a 100% retention rate.

And it is infectious - here TK experiments with the new toy, in another context:

I responded to you via board email. I hope you get it. Sometimes wiki software doesn't send stuff, it seems, for people have complained here of that, and I have seen the same complaints at WP. That is why so many of us prefer the 21st Century convention of swapping email addresses and using instant messaging, like 80%+ of Internet users do. - Sysop-TK 21:15, 25 May 2007 (EDT)[14]

In a similar vein,

* I am almost certain there is absolutely not [sic] sincerity in anything you typed above" - Sysop-TK 02:11, 23 May 2007 (EDT)

PS: that's not a footnote asterisk, it's an "emphasis" asterisk, in case you aren't familiar with this particular Sysop's style.

Whilst more sophisticated statistical methods require testing to see if results and conclusions are valid, Schlafly statistics require only Andy to look at the numbers and he can see the difference:

When the rates are vastly different, as in the case of lung cancer and cigarettes, conclusions can and should be drawn immediately based on the vast differences in the rates. Of course further investigation is welcome, but unlikely to alter the obvious conclusion. - Aschlafly[15]

Schlafly statistics also differs from frequentist or Bayesian statistical methods in that data mining is not needed:

Female rock stars likely to admit to breast cancer (e.g., due to a performance schedule) number on the magnitude of, at most, 50; female movie stars under 35 are perhaps a 100-200, at most; and other female actresses are perhaps another 100-200. Just look at how many albums and movies are released each year. Also, note that many stars go out of their way to conceal and deny health problems, understandably so. If you dispute any of these numbers, then I'd like to hear why. - Aschlafly[16]

I'm not disputing them, I'm asking where they came from. So you looked up how many albums and movies are typically released in a year and extrapolated from that? Murray[17]

Schlafly statistics are superior to any other method of analysing data as it would save millions of lives:

That provides a ballpark estimate. Given that the incidence in the Hollywood community is an order of magnitude larger than the general population, ballpark estimates are enough to reveal a problem.

Note that a ballpark estimate would have -- and should have -- demonstrated that cigarettes cause lung cancer a half century before people accepted it. Millions of lives would have been saved by acting on the evidence available rather than demanding unnecessary statistical detail. Surely you don't defend that. - Aschlafly[18]

You will spend a certain amount of time preparing for the midterm exam. Call that amount of time "x". How you allocate that time to different areas of 1500-1877 will make a difference on how well you do on the exam. If you spend 90% of x on the period between 1500 and 1700, then you will do poorly on 90% of the questions, because they will be from the period 1700 to 1877. You would have done far better to spend the 90% of x on the time period that will have 90% of the questions.[23]

So, x=Time spent studying; Period between 1500 and 1700 = y; Period between 1700 and 1877 = z. Rebuilding Schlafly's equation, we get the ideal value of x=z*0.9-y*0.9. No, wait. If y*0.9=x, then ..no, hang on. I'll start again. X is 90% of 1877. No, that's not right. Wait. The difference in years of y=200, and the time period of z is 177. So y>z. So the factor of 90% can't be fairly applied to both periods. Can it? I don't know, I give up.

What he fails to understand here is that X = 7 and v = 9, therefore, 90% of questions will in fact be from the year 1880, making your studying of anything, especially from a liberal university, useless.

A corollary to the Schlafly Statistic is the Schlafly Study, where Andy claims that his views are supported by "a majority of studies", and since this is so obvious, there is no need to cite them.

Your posting above is false and misleading. The Lancet entry, for example, is not a study at all. I doubt many of the others are either. Virtually all statistically significant studies have shown an increase risked [sic], as does logic. The increased risk was shown before abortion became politically controversial and before the abortion industry became so powerful.--Aschlafly 23:28, 10 April 2007 (EDT) (emphasis added; see here for the full conversation)

What Mr. Schlafly apparently does not understand (and what was pointed out to him in the same discussion) was that all significant studies show an increased risk because that is the definition of significance. A non-significant result only allows the conclusion "no significant effect was found" However if multiple studies are unable to find a significant effect despite superior levels of statistical power this suggests significant findings are due to random variation in the sample (i.e. the finding of significance is the product of random variation between the two conditions).

↑ And even with a zero probability an event can still occur. Take for example an infinite plan dartboard with infinitesimally thin lines. The probability of hitting any line is 1, the odds of hitting a particular line is 0, but every time you throw a dart it must hit a line so it does occur. The only way you can be guaranteed something is not going to occur, is for it not to be in the state space at all.